论域自调整模糊PID控制算法设计与仿真

通过建立基于论域调整的自适应模糊PID控制算法对电控直列泵柴油机转速控制的位置环PID参数进行自整定.根据执行器的工作特性建立了执行器模型,与控制算法模型组成闭环进行仿真验证.通过与普通增量式PID控制算法的仿真结果进行比较,结果表明该控制算法在柴油机全工况范围内油量调节齿杆的控制效果优于普通增量式PID控制的效果.     

对硝基苄基二乙基羟乙基溴化铵的合成

介绍了无水丙酮作溶剂合成对硝基苄基二乙基羟乙基溴化铵的方法，该方法操作简单，产率达９３％。     

一种适于Internet拥塞控制的自校正队列管理算法

1.引言近年来,随着计算机和网络技术的迅猛发展以及多媒体应用的急剧增加,人们对Internet的服务质量提出了更高的要求。虽然目前TCP所采用的基于窗口的端到端拥塞控制机制对Internet的鲁棒性起到了关键性的作用。但传统的去尾(drop-tail)先进先出(FIFO)的队列管理方式,不可避免地会导致过高的传输延迟和延迟抖动。为适应越来越多的实体媒体传输的要求,人们开始研究更为有效的队列管理算法,从而使网络在采用TCP拥塞控制算法的基础上,实现效率最高并尽可能减小路由器中的平均队列长度,即主动队列管理技     

基于神经网络的静止无功补偿器自校正内模控制

本文利用ＢＰ神经网络逼近受控系统的动态及其逆动态，设计了一种静止无功补偿装置的自校正内模控制器。该控制器的正模型和逆模型都以三层ＢＰ神经网络为主体，实现对ＳＶＣ及电网的动态描述和对ＳＶＣ的控制。     

次同步谐振输出反馈的最优励磁控制

通过分析提出了一种SSR(Subsychronous Resonance)励磁控制器的实用设计方法,通过直接优化输出反馈增益泛函的某一特定性能指标求取最优输出反馈参数.根据SSR系统的特点,给出输出反馈信息的选择原则,用一种有效的降阶方法将一24阶SSR模型降至15阶,设计了5个信号用于反馈的最优励磁控制器.特征值分析与计算机仿真结果表明,采用该方法设计的最优输出反馈励磁控制器对于大范围的串联电容补偿度,均能提供恰当的阻尼抑制SSR的全部振荡模式,从而使得多机系统SSR励磁控制器的设计问题得到较好的解决。     

一种利用晶闸管的新型交流调功器

本文提出一种利用晶闸管的新型交流调动技术。与开关式交流调压技术(The switchingAC Voltage Regulation Technique)和整周期触发技术(The Integral-cycle TriggeringTechnique)相比,具有无波形畸变、调节速度快、调节偏差小等优点,可应用于某些电阻加热装置上。     

基于自适应模糊控制的Web带宽区分服务

基于线程/进程分配的Web区分服务策略仅仅关注连接延迟,而在服务器带宽受限的情况下,由于处理延迟成为客户端总延迟的主导部分,因此区分效果很差.提出的基于带宽调节的区分服务策略,通过两级自适应模糊控制,调整服务于不同优先级请求的虚拟主机的带宽配额,从而控制处理延迟,实现比例延迟保证.经稳定性分析与实验验证,这种方法取得了良好的效果,相对于静态模糊控制,其延迟比与期望值的方差减少了40%.     

波形分析法模糊自整定PID控制应用研究

在分析空调系统常规PID自整定控制局限性的基础上,指出利用偏差及偏差变化量作为PID参数调节判据是常规PID自整定控制难以取得理想控制效果的根源,从而引入波形分析法求解控制过程的性能指标即超调量与调节时间,并提出利用性能指标作为PID参数调节的判据将克服常规PID自整定控制的局限性.对性能指标与PID调节器参数的关系进行了分析,给出了根据性能指标进行PID调节的模糊控制规则,基于上述规则建立了空调机组模糊PID控制系统数学模型,并进行了仿真.仿真结果表明,该波形分析法模糊自整定PID控制可以较好解决如空调系统等大、纯滞后受控对象的控制,得到较理想的控制性能指标,并因此获得较高的经济效益.     

Self-tuning decoupled information fusion Wiener state component filters and their convergence

For the multisensor systems with unknown noise variances, using the modern time series analysis method, based on on-line identification of the moving average (MA) innovation models, and based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances are obtained, and under linear minimum variance optimal information fusion criterion weighted by scalars for state components, a class of self-tuning decoupled fusion Wiener filters is presented. It realizes the self-tuning decoupled local Wiener filters and self-tuning decoupled fused Wiener filters for the state components. A new concept of convergence in a realization is presented, which is weaker than the convergence with probability one. The dynamic error system analysis (DESA) method is presented, by which the problem of convergence in a realization for self-tuning fusers is transformed into the stability problems of non-homogeneous difference equations, and the decision criterions of the stability are also presented. It is strictly proved that if the parameter estimation of the MA innovation models is consistent and if the measurement process is bounded in a realization or with probability one, then the self-tuning fusers will converge to the optimal fusers in a realization or with probability one, so that they have the asymptotic optimality. They can deal with the systems with the non-stationary or Gaussian measurement processes. They can reduce the computational burden, and are suitable for real time applications. A simulation example for a target tracking system with 3-sensor shows their effectiveness.     

A new learning schema based on support vector for multi-classification

A novel learning schema SVCMR based on support vector is proposed in this paper to address M-class classification issue. It creates a tree-shaped decision frame where M/2 nodes are constructed with the three-separation model as the basic classifier. A class selection rule is defined to ensure basic classifiers be trained in turn on pair of classes with maximum feature distance. Class contours are extracted as data representatives to reduce training set size. Another point is that parameters involved in SVCMR are learned from data neighborhood, which brings adaptation to various datasets and avoids pricy cost spent on searching parameter spaces. Experiments on real datasets demonstrate the performance of SVCMR can be competitive to those state-of-the-art classifiers but with the higher effectiveness than them.